Use of Hierarchical Dirichlet Processes to Integrate Dependent Observations from Multiple Disparate Sensors for Tracking

Bahman Moraffah, Cesar Brito, Bindya Venkatesh, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We consider the problem of tracking a target by integrating observations from multiple disparate sources in a multimodal sensing system. Based on the sensing modalities, these observations are associated with different measurement models. They are also statistically dependent if acquired synchronously while capturing the same scene. Although dependency among measurements is largely overlooked, improved performance can be achieved if this additional information is modeled and incorporated in the tracking formulation. This paper employs a hierarchical Dirichlet process mixture to model the data dependency and extract the time-varying cardinality of the measurements of each sensor. The hierarchical Dirichlet process framework provides a joint measurement density model that is integrated with Bayesian tracking methods to estimate the target state information.

Original languageEnglish (US)
Title of host publicationFUSION 2019 - 22nd International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780996452786
StatePublished - Jul 2019
Event22nd International Conference on Information Fusion, FUSION 2019 - Ottawa, Canada
Duration: Jul 2 2019Jul 5 2019

Publication series

NameFUSION 2019 - 22nd International Conference on Information Fusion

Conference

Conference22nd International Conference on Information Fusion, FUSION 2019
CountryCanada
CityOttawa
Period7/2/197/5/19

Keywords

  • dependent measurements
  • Hierarchical Dirichlet process
  • Markov chain Monte Carlo sampling
  • nonparametric Bayesian methods
  • target tracking
  • time-varying cardinality

ASJC Scopus subject areas

  • Information Systems
  • Instrumentation

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